Farhan Feroz University of Cambridge, United Kingdom
Main Content
Astrophysics and cosmology have increasingly become data driven with the availability of large amount of high quality data from missions like WMAP, Planck and LHC. This has resulted in the development of many innovative methods for performing robust statistical analyses. MultiNest is a Bayesian inference algorithm, based on nested sampling, which has been applied successfully to numerous challenging problems in cosmology and astroparticle physics due to its capability of efficiently exploring multi-modal parameter spaces. MultiNest can also calculate the Bayesian evidence and therefore provides means to carry out Bayesian model selection. I will give a brief description of this algorithm and review its applications in astrophysics and cosmology. I will also describe some recent work on developing new methods for greatly accelerating statistical analyses, in particular by combining neural networks and nested sampling methods. These approaches are generic in nature and may therefore be applied beyond astrophysics.